KISS: Stochastic Packet Inspection Classifier for UDP Traffic

This paper proposes KISS, a novel Internet classifica- tion engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming appli- cations, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square-like test, which extracts the protocol "format," but ignores the protocol "semantic" and "synchronization" rules. The signatures feed a decision process based either on the geometric distance among samples, or on Sup- port Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asym- metry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server proto- cols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are al- most perfect when dealing with new P2P streaming applications

Malware detection techniques for mobile devices

Mobile devices have become very popular nowadays, due to its portability and high performance, a mobile device became a must device for persons using information and communication technologies. In addition to hardware rapid evolution, mobile applications are also increasing in their complexity and performance to cover most needs of their users. Both software and hardware design focused on increasing performance and the working hours of a mobile device. Different mobile operating systems are being used today with different platforms and different market shares. Like all information systems, mobile systems are prone to malware attacks. Due to the personality feature of mobile devices, malware detection is very important and is a must tool in each device to protect private data and mitigate attacks. In this paper, analysis of different malware detection techniques used for mobile operating systems is provides. The focus of the analysis will be on the to two competing mobile operating systems - Android and iOS. Finally, an assessment of each technique and a summary of its advantages and disadvantages is provided. The aim of the work is to establish a basis for developing a mobile malware detection tool based on user profiling.Comment: 11 pages, 6 figure

LPKI - A Lightweight Public Key Infrastructure for the Mobile Environments

The non-repudiation as an essential requirement of many applications can be provided by the asymmetric key model. With the evolution of new applications such as mobile commerce, it is essential to provide secure and efficient solutions for the mobile environments. The traditional public key cryptography involves huge computational costs and is not so suitable for the resource-constrained platforms. The elliptic curve-based approaches as the newer solutions require certain considerations that are not taken into account in the traditional public key infrastructures. The main contribution of this paper is to introduce a Lightweight Public Key Infrastructure (LPKI) for the constrained platforms such as mobile phones. It takes advantages of elliptic curve cryptography and signcryption to decrease the computational costs and communication overheads, and adapting to the constraints. All the computational costs of required validations can be eliminated from end-entities by introduction of a validation authority to the introduced infrastructure and delegating validations to such a component. LPKI is so suitable for mobile environments and for applications such as mobile commerce where the security is the great concern.Comment: 6 Pages, 6 Figure